Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis - Adil, Asif; Kumar, Vijay; Jan, Arif Tasleem; Asger, Mohammed (Frontiers in Neuroscience, 2021)
Rapid cost drops and advancements in next-generation sequencing has made profiling of millions of cells at individual level a conventional practice in the scientific laboratories worldwide. Single Cell transcriptomics (SC-RNA-seq) has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights to the scientific communities. However, due to low starting materials the SC-RNA-seq data faces various computational challenges viz., Normalization, Differential Gene Expression (DEG) Analysis, dimensionality reduction etc. Also, new methods like 10x Chromium are able to profile millions of cells in parallel, which creates a huge amount of data. Thus single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation and data generation. Also, we highlight the prominent computational challenges which require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomic data as big data problem.